Filling data gaps in global scale biogeography and biodiversity applications with natural history information

In spite of the massive amount of data already collected on global patterns of species occurrence, the primary impediment to understanding or forecasting biodiversity patterns remains the paucity of data for many species. Recent advances in ecoinformatics and statistical methods provide new opportunities to bridge these gaps using existing natural history information and suggest priorities for future collections. I will discuss how these data can (1) help detect responses to global change by generating time series data; (2) enable demographic models by recording improved spatial precision of individuals, and (3) bridge gaps with auxiliary contextual data (abundance, environment, co-occurring species).